10,497 research outputs found
Managing Service-Heterogeneity using Osmotic Computing
Computational resource provisioning that is closer to a user is becoming
increasingly important, with a rise in the number of devices making continuous
service requests and with the significant recent take up of latency-sensitive
applications, such as streaming and real-time data processing. Fog computing
provides a solution to such types of applications by bridging the gap between
the user and public/private cloud infrastructure via the inclusion of a "fog"
layer. Such approach is capable of reducing the overall processing latency, but
the issues of redundancy, cost-effectiveness in utilizing such computing
infrastructure and handling services on the basis of a difference in their
characteristics remain. This difference in characteristics of services because
of variations in the requirement of computational resources and processes is
termed as service heterogeneity. A potential solution to these issues is the
use of Osmotic Computing -- a recently introduced paradigm that allows division
of services on the basis of their resource usage, based on parameters such as
energy, load, processing time on a data center vs. a network edge resource.
Service provisioning can then be divided across different layers of a
computational infrastructure, from edge devices, in-transit nodes, and a data
center, and supported through an Osmotic software layer. In this paper, a
fitness-based Osmosis algorithm is proposed to provide support for osmotic
computing by making more effective use of existing Fog server resources. The
proposed approach is capable of efficiently distributing and allocating
services by following the principle of osmosis. The results are presented using
numerical simulations demonstrating gains in terms of lower allocation time and
a higher probability of services being handled with high resource utilization.Comment: 7 pages, 4 Figures, International Conference on Communication,
Management and Information Technology (ICCMIT 2017), At Warsaw, Poland, 3-5
April 2017, http://www.iccmit.net/ (Best Paper Award
Fog Computing: A Taxonomy, Survey and Future Directions
In recent years, the number of Internet of Things (IoT) devices/sensors has
increased to a great extent. To support the computational demand of real-time
latency-sensitive applications of largely geo-distributed IoT devices/sensors,
a new computing paradigm named "Fog computing" has been introduced. Generally,
Fog computing resides closer to the IoT devices/sensors and extends the
Cloud-based computing, storage and networking facilities. In this chapter, we
comprehensively analyse the challenges in Fogs acting as an intermediate layer
between IoT devices/ sensors and Cloud datacentres and review the current
developments in this field. We present a taxonomy of Fog computing according to
the identified challenges and its key features.We also map the existing works
to the taxonomy in order to identify current research gaps in the area of Fog
computing. Moreover, based on the observations, we propose future directions
for research
ENORM: A Framework For Edge NOde Resource Management
Current computing techniques using the cloud as a centralised server will
become untenable as billions of devices get connected to the Internet. This
raises the need for fog computing, which leverages computing at the edge of the
network on nodes, such as routers, base stations and switches, along with the
cloud. However, to realise fog computing the challenge of managing edge nodes
will need to be addressed. This paper is motivated to address the resource
management challenge. We develop the first framework to manage edge nodes,
namely the Edge NOde Resource Management (ENORM) framework. Mechanisms for
provisioning and auto-scaling edge node resources are proposed. The feasibility
of the framework is demonstrated on a PokeMon Go-like online game use-case. The
benefits of using ENORM are observed by reduced application latency between 20%
- 80% and reduced data transfer and communication frequency between the edge
node and the cloud by up to 95\%. These results highlight the potential of fog
computing for improving the quality of service and experience.Comment: 14 pages; accepted to IEEE Transactions on Services Computing on 12
September 201
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